Analysis of Transient Stability through a Novel Algorithm with Optimization under Contingency Conditions

Kumar Reddy Cheepati, Suresh Babu Daram, Ch. Rami Reddy (), T. Mariprasanth, Basem Alamri () and Mohammed Alqarni
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Kumar Reddy Cheepati: Electrical and Electronics Engineering, KSRM College of Engineering, Kadapa 516005, India
Suresh Babu Daram: Electrical and Electronics Engineering, School of Engineering, Mohan Babu University, Tirupati 517102, India
Ch. Rami Reddy: Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan
T. Mariprasanth: Electrical and Electronics Engineering, KSRM College of Engineering, Kadapa 516005, India
Basem Alamri: Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Mohammed Alqarni: Department of Electrical Engineering, College of Engineering, University of Business and Technology (UBT), Jeddah 21361, Saudi Arabia

Energies, 2024, vol. 17, issue 17, 1-26

Abstract: Predicting the need for modeling and solutions is one of the largest difficulties in the electricity system. The static-constrained solution, which is not always powerful, is provided by the Gradient Method Power Flow (GMPF). Another benefit of using both dynamic and transient restrictions is that GMPF will increase transient stability against faults. The system is observed under contingency situations using the Dynamic Stability for Constrained Gradient Method Power Flow (DSCGMPF). The population optimization technique is the foundation of a recent algorithm called Training Learning Based Optimization (TLBO). The TLBO-based approach for obtaining DSCGMPF is implemented in this work. The total system losses and the cost of the individual generators have been optimized. Analysis of the stability limits under contingency conditions has been conducted as well. To illustrate the suggested approaches, a Standard 3 machine 5-bus system is simulated using the MATLAB 2022B platform.

Keywords: transient stability; dynamic stability; contingency condition; gradient method power flow; training learning-based optimization; constrained gradient method power flow (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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